OpenMed is local-first healthcare AI software that extracts clinical information and removes personally identifying details from clinical text on hardware controlled by the user. Healthcare developers use its Python runtime, Apple Silicon and mobile SDKs, and browser support for on-device clinical NER and PII de-identification.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add maziyarpanahi/openmed --skill building-gold-corpusgit clone --depth 1 https://github.com/maziyarpanahi/openmedWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/maziyarpanahi/openmed/building-gold-corpus)<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/building-gold-corpus"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/building-gold-corpus/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/building-gold-corpus"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/building-gold-corpus.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00159 | $0.01713 |
| Opus 5 | $0.00079 | $0.00856 |
| Sonnet 5 | $0.00032 | $0.00343 |
| Haiku 4.5 | $0.00016 | $0.00171 |
Grade A, and why
building-gold-corpus scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building a Gold Corpus
You can't evaluate what you can't measure against. This skill scaffolds a gold-standard annotation project whose output drops straight into the OpenMed eval harness as fixtures. The hard rule: anything committed to the repo is synthetic. Licensed clinical corpora (i2b2, n2c2, MIMIC) are DUA-gated — use them at eval time from the user's own copy, never check them in.
When to use this skill
- You need eval fixtures for
benchmarking-clinical-nerorevaluating-with-leakage-gatesand have none. - You're standing up an annotation effort: schema, guidelines, tool config.
- You need disciplined train/dev/test splits with no leakage between them.
- You want a small synthetic golden set you can commit and gate on in CI.
The OpenMed fixture shape (your target output)
Annotations must serialize to character-offset spans the harness understands:
{
"fixtures": [
{
"id": "synthetic-0001",
"language": "en",
"text": "Ms. Jane Roe (MRN 0000000) seen 2099-01-02 for type 2 diabetes.",
"gold_spans": [
{"start": 4, "end": 12, "label": "PERSON"},
{"start": 18, "end": 25, "label": "ID_NUM"},
{"start": 32, "end": 42, "label": "DATE"},
{"start": 47, "end": 62, "label": "DISEASE"}
]
}
]
}
openmed.eval.harness.load_fixtures accepts a top-level list or a {"fixtures": [...]} mapping. Offsets are character indices into text; labels are
OpenMed-canonical.
Quick start — scaffold the project
eval/
gold/
guidelines.md # annotation manual + edge-case decisions
label_schema.json # canonical labels + definitions + examples
synthetic/ # COMMITTED synthetic fixtures (CI-gateable)
train.json
dev.json
test.json
external/ # GITIGNORED: licensed DUA corpora, eval-only
.gitignore # * (never commit i2b2/n2c2/MIMIC)
Verify your synthetic fixtures load and validate spans before you trust them:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 140 lines · 159 tokens per session scan A af0190ab9f3e
building-gold-corpus is a skill published in the GitHub repository maziyarpanahi/openmed (5,290 stars, last pushed yesterday), licensed Apache-2.0. It adds 159 tokens to every session and 1,713 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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